Search Results for author:
Found 12 papers, 5 papers with code
VINS-Mono: A Robust and Versatile Monocular Visual-Inertial State Estimator
A monocular visual-inertial system (VINS), consisting of a camera and a low-cost inertial measurement unit (IMU), forms the minimum sensor suite for metric six degrees-of-freedom (DOF) state estimation.
Robotics
Stereo R-CNN based 3D Object Detection for Autonomous Driving
Our method, called Stereo R-CNN, extends Faster R-CNN for stereo inputs to simultaneously detect and associate object in left and right images.
3D Object Detection From Stereo Images
Autonomous Driving
+3
Trajectory Prediction with Graph-based Dual-scale Context Fusion
In this paper, we present a graph-based trajectory prediction network named the Dual Scale Predictor (DSP), which encodes both the static and dynamical driving context in a hierarchical manner.
SIMPL: A Simple and Efficient Multi-agent Motion Prediction Baseline for Autonomous Driving
This paper presents a Simple and effIcient Motion Prediction baseLine (SIMPL) for autonomous vehicles.
MonoJSG: Joint Semantic and Geometric Cost Volume for Monocular 3D Object Detection
Based on the object depth, the dense coordinates patch together with the corresponding object features is reprojected to the image space to build a cost volume in a joint semantic and geometric error manner.
Stereo Vision-based Semantic 3D Object and Ego-motion Tracking for Autonomous Driving
We propose a stereo vision-based approach for tracking the camera ego-motion and 3D semantic objects in dynamic autonomous driving scenarios.
Multi-Sensor 3D Object Box Refinement for Autonomous Driving
We propose a 3D object detection system with multi-sensor refinement in the context of autonomous driving.
Joint Spatial-Temporal Optimization for Stereo 3D Object Tracking
Directly learning multiple 3D objects motion from sequential images is difficult, while the geometric bundle adjustment lacks the ability to localize the invisible object centroid.
Tracking from Patterns: Learning Corresponding Patterns in Point Clouds for 3D Object Tracking
A robust 3D object tracker which continuously tracks surrounding objects and estimates their trajectories is key for self-driving vehicles.
Temporal Point Cloud Completion with Pose Disturbance
With the help of gated recovery units(GRU) and attention mechanisms as temporal units, we propose a point cloud completion framework that accepts a sequence of unaligned and sparse inputs, and outputs consistent and aligned point clouds.
You Only Label Once: 3D Box Adaptation from Point Cloud to Image via Semi-Supervised Learning
The image-based 3D object detection task expects that the predicted 3D bounding box has a ``tightness'' projection (also referred to as cuboid), which fits the object contour well on the image while still keeping the geometric attribute on the 3D space, e. g., physical dimension, pairwise orthogonal, etc.
Are All Point Clouds Suitable for Completion? Weakly Supervised Quality Evaluation Network for Point Cloud Completion
In this paper, we propose a quality evaluation network to score the point clouds and help judge the quality of the point cloud before applying the completion model.
